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The dermis and epidermis, crucial structural layers of the skin, encompass appendages, hair follicles (HFs), and intricate cellular heterogeneity. However, an integrated spatiotemporal transcriptomic atlas of embryonic skin has not yet been described and would be invaluable for studying skin‐related diseases in humans. Here, single‐cell and spatial transcriptomic analyses are performed on skin samples of normal and hairless fetal pigs across four developmental periods. The cross‐species comparison of skin cells illustrated that the pig epidermis is more representative of the human epidermis than mice epidermis. Moreover, Phenome‐wide association study analysis revealed that the conserved genes between pigs and humans are strongly associated with human skin‐related diseases. In the epidermis, two lineage differentiation trajectories describe hair follicle (HF) morphogenesis and epidermal development. By comparing normal and hairless fetal pigs, it is found that the hair placode (Pc), the most characteristic initial structure in HFs, arises from progenitor‐like OGN+/UCHL1+ cells. These progenitors appear earlier in development than the previously described early Pc cells and exhibit abnormal proliferation and migration during differentiation in hairless pigs. The study provides a valuable resource for in‐depth insights into HF development, which may serve as a key reference atlas for studying human skin disease etiology using porcine models. [ABSTRACT FROM AUTHOR]

Objectives: CD4+ T cell helper and regulatory function in human cancers has been well characterised. However, the definition of tumor‐infiltrating CD4+ T cell exhaustion and how it contributes to the immune response and disease progression in human gastric cancer (GC) remain largely unknown. Methods: A total of 128 GC patients were enrolled in the study. The expression of CD39 and PD‐1 on CD4+ T cells in the different samples was analysed by flow cytometry. GC‐infiltrating CD4+ T cell subpopulations based on CD39 expression were phenotypically and functionally assessed. The role of CD39 in the immune response of GC‐infiltrating T cells was investigated by inhibiting CD39 enzymatic activity. Results: In comparison with CD4+ T cells from the non‐tumor tissues, significantly more GC‐infiltrating CD4+ T cells expressed CD39. Most GC‐infiltrating CD39+CD4+ T cells exhibited CD45RA−CCR7− effector–memory phenotype expressing more exhaustion‐associated inhibitory molecules and transcription factors and produced less TNF‐α, IFN‐γ and cytolytic molecules than their CD39−CD4+ counterparts. Moreover, ex vivo inhibition of CD39 enzymatic activity enhanced their functional potential reflected by TNF‐α and IFN‐γ production. Finally, increased percentages of GC‐infiltrating CD39+CD4+ T cells were positively associated with disease progression and patients' poorer overall survival. Conclusion: Our study demonstrates that CD39 expression defines GC‐infiltrating CD4+ T cell exhaustion and their immunosuppressive function. Targeting CD39 may be a promising therapeutic strategy for treating GC patients. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is one of the core driving forces for the next breeding stage, and Breeding 4.0. Genotype matrix based on single‐nucleotide polymorphisms (SNPs) is often used in ML for genome‐to‐phenotype prediction. Genotype matrix has an inherent defect, as the feature spaces it generates across different individuals or groups are inconsistent, and this hinders the application of ML. To overcome the challenge, a genome descriptor, Genic SNPs Composition Tool (GSCtool) is developed, which counts the number of SNPs in each gene of the genome so the dimension of the feature vectors equals the number of annotated genes in a species. Compared to using the genotype matrix, using GSCtool significantly decreases the model training time and has a higher accuracy of phenotype prediction. GSCtool also achieves good performance in variety identification, which is useful in crop variety protection. In general, GSCtool will help facilitate the application and study of genomic ML. The source code and test data of GSCtool are freely available at https://github.com/SZJhacker/GSCtool and https://gitee.com/shenzijie/GSCtool. [ABSTRACT FROM AUTHOR]

Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep hypergraph learning framework for the prediction of peptide secondary structures and the exploration of downstream tasks. The framework includes a novel interpretable deep hypergraph multi‐head attention network that uses residue‐based reasoning for structure prediction. The algorithm can incorporate sequential semantic information from large‐scale biological corpus and structural semantic information from multi‐scale structural segmentation, leading to better accuracy and interpretability even with extremely short peptides. The interpretable models are able to highlight the reasoning of structural feature representations and the classification of secondary substructures. The importance of secondary structures in peptide tertiary structure reconstruction and downstream functional analysis is further demonstrated, highlighting the versatility of our models. To facilitate the use of the model, an online server is established which is accessible via http://inner.wei‐group.net/PHAT/. The work is expected to assist in the design of functional peptides and contribute to the advancement of structural biology research. [ABSTRACT FROM AUTHOR]

A novel reaching law‐based sliding mode controller is proposed for a permanent magnet synchronous motor (PMSM) speed regulation system with uncertainties and unknown load torque in this paper. The proposed reaching law is the improvement of the traditional power rate reaching law (PRL) by using a simple tuning function of the sliding variable. The tuning function is designed such that the reaching speed is fast when the system states are far away from the sliding surface, and vice versa. Theoretical analysis shows that the reaching time of the proposed reaching law is always shorter than that of the traditional PRL with the same gains. Moreover, unlike the traditional PRL and some existing auto‐tuning PRLs, the proposed reaching law can provide globally bounded reaching time independently on the initial conditions, and the reaching time can be effectively reduced by tuning the reaching law gains. Based on this novel reaching law, a disturbance observer is designed to estimate the total disturbance, and then based on the estimated disturbance and the novel reaching law, a sliding mode speed controller is designed for the robust control of PMSM speed regulation system. Simulations and experiments are carried out to demonstrate the superiority of the proposed control method. [ABSTRACT FROM AUTHOR]

A novel reaching law based sliding mode control of PMSM speed regulation system with uncertainties and unknown external disturbance is proposed in this paper. The proposed reaching law mainly consists of a time‐varying gain and a fractional power term of the sliding variable. And the time‐varying gain is auto‐tuned by the sliding variable such that it is large when the sliding variable is far away from the sliding surface to speed‐up the reaching process, and vice versa. Unlike the existing auto‐tuning power rate reaching law (PRRL), the proposed reaching law provides a guaranteed reaching time independently on initial conditions. Moreover, the reaching time can be effectively reduced by tuning the gains of the reaching law. Based on this new reaching law, a disturbance observer is designed to estimate the unknown total disturbance and a sliding mode controller is designed for the robust control of PMSM speed regulation system based on the estimated disturbance. Simulations and experiments are carried out to verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]